Predicting structural disruption of proteins caused by crossover

We present a machine learning model that predicts a structural disruption score from a protein’s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision. ©2005 IEEE

Predicting peroxisomal proteins

PTS1 proteins are peroxisomal matrix proteins that have a well conserved targeting motif at the C-terminal end. However, this motif is present in many non peroxisomal proteins as well, thus predicting peroxisomal proteins involves differentiating fake PTS1 signals from actual ones. In this paper we report on the development of an SVM classifier with a separately trained logistic output function. The model uses an input window containing 12 consecutive residues at the C-terminus and the amino acid composition of the full sequence. The final model gives a Matthews Correlation Coefficient of 0.77, representing an increase of 54% compared with the well-known PeroxiP predictor. We test the model by applying it to several proteomes of eukaryotes for which there is no evidence of a peroxisome, producing a false positive rate of 0.088%.

Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway

Peroxisomes are small subcellular compartments that utilize proteins manufactured in the cytoplasm. Proteins use one of two peroxisomal import pathways. This paper presents a simple evolutionary search for a motif that describes the signal used by one of the two pathways: PTS2. The evolved motif has a discriminative accuracy exceeding previously manually curated motifs and can be used to screen genomic data for putative peroxisomal proteins.

Active membrane transport and receptor proteins from bacteria

A general strategy for the expression of bacterial membrane transport and receptor genes in Escherichia coli is described. Expression is amplified so that the encoded proteins comprise 5-35% of E. coli inner membrane protein. Depending upon their topology, proteins are produced with RGSH6 or a Strep tag at the C-terminus. These enable purification in mg quantities for crystallization and NMR studies. Examples of one nutrient uptake and one multidrug extrusion protein from Helicobacter pylori are described. This strategy is successful for membrane proteins from H. pylori, E. coli, Enterococcus faecalis, Bacillus subtilis, Staphylococcus aureus, Microbacterium liquefaciens, Brucella abortus, Brucella melitensis, Campylobacter jejuni, Neisseria meningitides, Streptomyces coelicolor and Rhodobacter sphaeroides. ©2005 Biochemical Society.

RAMPs and the modulation of G-protein coupled receptors

Receptor activity modifying proteins (RAMPs) are a family of three proteins that can interact with G-protein coupled receptors (GPCRs) to create new receptors and also alter the signalling and trafficing of existing receptors. There are frequently changes in RAMP expression in cardiovascular disease RAMPs represent important drug targets.

Production of membrane proteins in yeast

Background Yeast is an important and versatile organism for studying membrane proteins. It is easy to cultivate and can perform higher eukaryote-like post-translational modifications. S. cerevisiae has a fully-sequenced genome and there are several collections of deletion strains available, whilst P. pastoris can produce very high cell densities (230 g/l). Results We have used both S. cerevisiae and P. pastoris to over-produce the following His6 and His10 carboxyl terminal fused membrane proteins. CD81 – 26 kDa tetraspanin protein (TAPA-1) that may play an important role in the regulation of lymphoma cell growth and may also act as the viral receptor for Hepatitis C-Virus. CD82 – 30 kDa tetraspanin protein that associates with CD4 or CD8 cells and delivers co-stimulatory signals for the TCR/CD3 pathway. MC4R – 37 kDa seven transmembrane G-protein coupled receptor, present on neurons in the hypothalamus region of the brain and predicted to have a role in the feast or fast signalling pathway. Adt2p – 34 kDa six transmembrane protein that catalyses the exchange of ADP and ATP across the yeast mitochondrial inner membrane. Conclusion We show that yeasts are flexible production organisms for a range of different membrane proteins. The yields are such that future structure-activity relationship studies can be initiated via reconstitution, crystallization for X-ray diffraction or NMR experiments.

Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.

Immunological and biochemical techniques in the analysis of tear proteins

This study is concerned with the analysis of tear proteins, paying particular attention to the state of the tears (e.g. non-stimulated, reflex, closed), created during sampling, and to assess their interactions with hydrogel contact lenses. The work has involved the use of a variety of biochemical and immunological analytical techniques for the measurement of proteins, (a), in tears, (b), on the contact lens, and (c), in the eluate of extracted lenses. Although a diverse range of tear components may contribute to contact lens spoilation, proteins were of particular interest in this study because of their theoretical potential for producing immunological reactions. Although normal host proteins in their natural state are generally not treated as dangerous or non-self, those which undergo denaturation or suffer a conformational change may provoke an excessive and unnecessary immune response. A novel on-lens cell based assay has been developed and exploited in order to study the role of the ubiquitous cell adhesion glycoprotein, vitronectin, in tears and contact lens wear under various parameters. Vitronectin, whose levels are known to increase in the closed eye environment and shown here to increase during contact lens wear, is an important immunoregulatory protein and may be a prominent marker of inflammatory activity. Immunodiffusion assays were developed and optimised for use in tear analysis, and in a series of subsequent studies used for example in the measurement of albumin, lactoferrin, IgA and IgG. The immunodiffusion assays were then applied in the estimation of the closed eye environment; an environment which has been described as sustaining a state of sub-clinical inflammation. The role and presence of a lesser understood and investigated protein, kininogen, was also estimated, in particular, in relation to contact lens wear. Difficulties arise when attempting to extract proteins from the contact lens in order to examine the individual nature of the proteins involved. These problems were partly alleviated with the use of the on-lens cell assay and a UV spectrophotometry assay, which can analyse the lens surface and bulk respectively, the latter yielding only total protein values. Various lens extraction methods were investigated to remove protein from the lens and the most efficient was employed in the analysis of lens extracts. Counter immunoelectrophoresis, an immunodiffusion assay, was then applied to the analysis of albumin, lactoferrin, IgA and IgG in the resultant eluates.

Reentrant condensation of proteins in solution induced by multivalent counterions

Negatively charged globular proteins in solution undergo a condensation upon adding trivalent counterions between two critical concentrations C* and C**, C*ged electrostatic interactions between multivalent cations and acidic residues, mechanistically different from the case of DNA. Small-angle x-ray scattering indicates a short-ranged attraction between counterion-bound proteins near C* and C**. Monte Carlo simulations (under these strong electrostatic coupling conditions) support an effective inversion of charge on surface side chains through binding of the multivalent counterions.

In silico identification of novel G protein coupled receptors

G-protein coupled receptors (GPCRs) are a superfamily of membrane integral proteins responsible for a large number of physiological functions. Approximately 50% of marketed drugs are targeted toward a GPCR. Despite showing a high degree of structural homology, there is a large variance in sequence within the GPCR superfamily which has lead to difficulties in identifying and classifying potential new GPCR proteins. Here the various computational techniques that can be used to characterize a novel GPCR protein are discussed, including both alignment-based and alignment-free approaches. In addition, the application of homology modeling to building the three-dimensional structures of GPCRs is described.